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Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

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Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

Deployment of Behind-TheMeter Energy Storage for 

Demand Charge Reduction

J. Neubauer and M. Simpson

Technical Report

NREL/TP-5400-63162

January 2015
NREL is a national laboratory of the U.S. Department of Energy

Office of Energy Efficiency & Renewable Energy

Operated by the Alliance for Sustainable Energy, LLC

This report is available at no cost from the National Renewable Energy

Laboratory (NREL) at www.nrel.gov/publications.

Contract No. DE-AC36-08GO28308

National Renewable Energy Laboratory

15013 Denver West Parkway

Golden, CO 80401

303-275-3000 • www.nrel.gov

Deployment of Behind-TheMeter Energy Storage for 

Demand Charge Reduction 

J. Neubauer and M. Simpson

Prepared under Task No. VTP2.6407

Technical Report

NREL/TP-5400-63162

January 2015
NOTICE

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express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of 

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trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, 

or favoring by the United States government or any agency thereof. The views and opinions of authors 

expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.

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Laboratory (NREL) at www.nrel.gov/publications.

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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL 

16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.

NREL prints on paper that contains recycled content. 
iii

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Acknowledgment

This study was supported by Dave Howell and Brian Cunningham of the Energy Storage, 

Vehicle Technologies Office, Office of Energy Efficiency and Renewable Energy, U.S. 

Department of Energy. Special thanks to Kandler Smith and Eric Wood for supporting 

development of the modeling tools employed herein, Tony Markel for his insight into V2G 

service, and Ahmad Pesaran for his continual guidance. 
iv

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List of Acronyms

BLAST Battery Lifetime Analysis and Simulation Tool 

DCM demand charge management 

GW gigawatt 

kW kilowatt 

kWh kilowatt per hour

MW megawatt

PEV plug-in electric vehicle

PV photovoltaic 

sq. ft. square feet

Wh watt-hour 
v 

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Executive Summary

Mandates and subsidies for energy storage, including customer-sited, behind-the-meter 

installations, are on the rise. Where utilities employ demand charge rate structures, the most 

economic use of energy storage for customers is often to reduce monthly maximum demand. 

This study identifies how economically motivated customers will use energy storage for demand 

charge reduction, as well as how this changes in the presence of on-site photovoltaic power 

generation, to investigate the possible effects of incentivizing increased quantities of behind-themeter storage. Utilizing historical solar irradiance and demand profiles, we simulate the impact 

of lithium-ion batteries operated under a peak-shaving control algorithm on electricity costs, then 

identify cost-optimal battery configurations and their impact on metered load. We find that 

small, short-duration batteries are most cost-effective regardless of solar power levels, serving to 

reduce short load spikes on the order of 2.5% of peak demand. While profitable to the customer, 

such action is unlikely to adequately benefit the utility as may be desired, thus highlighting the 

need for modified utility rate structures or properly structured incentives. 
vi 

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Table of Contents

List of Figures ........................................................................................................................................... vii

List of Tables ............................................................................................................................................. vii

1 Introduction........................................................................................................................................... 1

2 Methods................................................................................................................................................. 2

2.1 Rate Structure................................................................................................................................ 2

2.2 Facility Loads................................................................................................................................ 3

2.3 Photovoltaic Generation................................................................................................................ 6

2.4 Battery Modeling & Control ......................................................................................................... 7

2.5 Design of Experiments.................................................................................................................. 7

3 Results................................................................................................................................................... 9

4 Discussion........................................................................................................................................... 16

4.1 Battery Sizing.............................................................................................................................. 16

4.2 Interaction with PV ..................................................................................................................... 17

4.3 Effect on Energy Consumption ................................................................................................... 17

4.4 Implications for Utilities ............................................................................................................. 17

4.5 Opportunities for Vehicle-to-Grid Service.................................................................................. 18

4.6 Opportunities for Battery Second Use......................................................................................... 19

4.7 Projecting Potential Market Size................................................................................................. 19

5 Conclusions ........................................................................................................................................ 20

References................................................................................................................................................. 21
vii 

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List of Figures

Figure 1. Illustration of the maximum energy storage required for maximum theoretical demand charge 

reduction (Emax) in the presence of a perfectly sinusoidal diurnal load profile........................ 3

Figure 2. Histograms of facility properties................................................................................................... 4

Figure 3. Distribution of Emax per square foot within sample facilities ........................................................ 5

Figure 4. Facility properties as a function of facility size and type .............................................................. 5

Figure 5. Illustration of the impact of solar intermittency and the noncoincidence of demand and PV 

generation on meter load. While the peak PV generation reaches ~160 kW, the peak metered 

load is reduced by only ~80 kW. ............................................................................................. 6

Figure 6. Normalized PV power production................................................................................................. 7

Figure 7. Minimum reduction in peak load versus installed battery power for all simulated facilities, 

available battery energy, and both with and without PV. ........................................................ 9

Figure 8. Increase in annual energy use versus minimum reduction in maximum monthly meter load for 

all simulated cases (both with and without PV)..................................................................... 10

Figure 9. Impact of battery operation on energy costs and demand charge costs for all simulated cases 

(both with and without PV).................................................................................................... 10

Figure 10. Payback period statistics by stored energy fraction for a 240 minute battery system, no PV 

generation............................................................................................................................... 11

Figure 11. Payback period statistics by stored energy fraction for a 30 minute battery system, no PV 

generation............................................................................................................................... 11

Figure 12. Payback period statistics by stored energy fraction for a 240-minute battery system, 50% PV 

generation............................................................................................................................... 12

Figure 13. Payback period statistics by stored energy fraction for a 30-minute battery system, 50% PV 

generation............................................................................................................................... 12

Figure 14. Median payback period across all 98 facilities as a function of battery system energy and 

duration .................................................................................................................................. 13

Figure 15. Best system energy and power levels for facilities without PV ................................................ 14

Figure 16. Distribution of payback-period-optimized battery system power levels (no PV) ..................... 14

Figure 17. Distribution of payback-period-optimized battery system energy levels (no PV). ................... 15

Figure 18. Effect of the presence of PV generation on the value of a 0.5% energy scale, 40minute power 

scale battery in a DCM role ................................................................................................... 15

Figure 19. Effect of behind-the-meter batteries on aggregated facility loads............................................. 18

List of Tables

Table 1. Southern California Edison’s TOU-GS-2 Option B Rate Structure [6].......................................... 2

Table 2. Southern California Edison’s TOU-GS-2 Option B Time Periods [7] ........................................... 3

Table 3. Energy and Power Ratings of Battery Systems .............................................................................. 8
1 

1 Introduction

Over the past few years, mandates and incentives for energy storage have increased dramatically. 

For example, in 2010 the California legislature passed Assembly Bill 2514 that resulted in the 

California Public Utilities Commission releasing a procurement target for 1.3 gigawatts (GW) of 

energy storage in the state by 2020 [1]. Approximately 15% of this allotment has been planned 

for customer-sited, behind-the-meter storage [2]. Customer-sited storage has been encouraged in 

California by the self-generation incentive program, which offers up to $1.62 per watt installed 

[3]. In New York, ConEdison offers up to $2.10 per watt installed for advanced batteries through 

an enhanced load reduction program [4]. 

Such incentives can make energy storage attractive to some customers; however, as they are 

often capped at some percentage of total installed cost, customers must have a viable value 

stream from the use of this storage to make the venture economically viable. Reduction of 

facility demand charges—a fee proportional to peak power rather than total energy—is a 

commonly referenced means to provide that value. In some instances, demand charges can 

constitute more than 50% of a commercial customer’s monthly electricity cost. While installation 

of behind-the-meter solar power generation decreases energy costs, solar intermittency due to 

cloud cover may cause the peak load—and thereby demand charges—to remain unaffected. This 

then makes demand charges an even larger fraction of the remaining electricity costs. Adding 

controllable behind-the-meter energy storage, however, can more predictably manage building 

peak demand, in turn reducing electricity costs. 

Optimizing the size and operation of an energy storage system providing demand charge 

management (DCM) service is important to yield a positive return on investment, even with 

incentives. The peak demand reduction achievable with an energy storage system depends 

heavily on the shape of a facility’s load profile, so the optimal configuration will be specific to 

both the customer and the amount of installed solar power capacity. The sensitivity of DCM 

value to the power and energy levels of installed solar power and energy storage systems is 

explored using the National Renewable Energy Laboratory’s Battery Lifetime Analysis and 

Simulation Tool (BLAST). BLAST is an optimal peak load reduction control algorithm for 

energy storage systems [5] and can be applied to historic solar power data and meter load data 

from multiple facilities for a broad range of energy storage system configurations. For each of 

these scenarios, the peak load reduction and electricity cost savings are computed. From the 

results, favorable energy storage system configurations are identified that maximize return on 

investment via minimizing the payback period. Subsequently, the impact of operating such a 

system on the aggregate meter load seen by the utility is assessed and compared to the objectives 

of utilities, and the need for properly structured incentives and modified rate structures to better 

meet those objectives is discussed. 
2 

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2 Methods

BLAST is a custom software package created in MATLAB to simulate the response of a battery 

installed behind-the-meter at a facility operated under various control strategies. Details on the 

full capability and functionality of the tool can be found in [5]. Within this study, we employ 

BLAST to explore the performance of batteries in a peak load reduction role in the presence of 

on-site photovoltaic (PV) generation. Herein PV generation is synthesized from solar irradiance 

data, then aggregated with historical demand data to create minute-by-minute meter load data, 

then applied to an equivalent circuit battery model and optimal peak shaving control algorithm to 

calculate the impact of energy storage on net meter load over a 12-month period. A real-world 

utility rate structure is then applied to the simulated total meter load to calculate annual 

electricity costs. Several facilities, each with varying sizes of PV power generation and energy 

storage systems, are simulated and compared to assess the economic benefit of such technology.

2.1 Rate Structure

After reviewing several utility rate structures with demand charge features, we ultimately elected

to employ Southern California Edison’s TOU-GS-2 option B rate structure circa April 2013 [6, 

7]. While this rate structure is not directly relevant to all of the facilities studied here (due to their 

size or geographic location), we have selected it for two main reasons: (1) its format is similar to 

many other demand charge rate structures via inclusion of continuously active facility demand 

charges and additional time-sensitive demand charges, and (2) the peak demands on record are 

reset at the end of each month (i.e., it uses a monthly ratchet). The relevant details of this 

structure are reproduced in Tables 1 and 2. 

Table 1. Southern California Edison’s TOU-GS-2 Option B Rate Structure [6] 

Charge Time Cost

Facility-related demand charge All $13.94/kW

Time-related demand charge Summer on-peak $16.20/kW

Summer mid-peak $ 4.95/kW

Summer off-peak $ 0.00/kW

Time-related energy charge Summer on-peak 14.66 ¢/kWh

Summer mid-peak 8.95 ¢/kWh

Summer off-peak 5.82 ¢/kWh

Winter on-peak 8.59 ¢/kWh

Winter mid-peak 8.59 ¢/kWh

Winter off-peak 5.47 ¢/kWh
3 

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Table 2. Southern California Edison’s TOU-GS-2 Option B Time Periods [7] 

Summer months 12:00 a.m. June 1 to 12:00 a.m. October 1

 Summer on-peak Noon to 6:00 pm

 Summer mid peak 8:00 a.m. to noon; 6:00 p.m. to 11:00 p.m.

 Summer off-peak 11:00 p.m. to 8:00 a.m.

Winter months 12:00 a.m. October 1 to 12:00 a.m. June 1

 Winter on-peak n/a

 Winter mid peak 8:00 a.m. to 9:00 p.m. 

 Winter off-peak 9:00 p.m. to 8:00 a.m. 

2.2 Facility Loads

Load data were acquired freely from EnerNOC’s online database for 98 commercial facilities

[8]. These data provide demand information over a continuous 12-month period in 5-minute 

increments, suitable for calculating annual electricity costs where demand charges are calculated 

for peak demand over a 15-minute period. Inspection of the data and conversation with an 

EnerNOC representative suggest that the data are purely demand—no PV power generation is 

included within this data. 

Along with the raw load data, information on the site location, facility square footage, and the 

nature of operations is also included. In addition to these metrics, a maximum energy storage 

value, Emax, was created specifically to capture the daily variability in a facility’s load profile by 

approximating the amount of energy storage required of a perfectly efficient battery to fully 

flatten the meter load (see Figure 1). If a consistent diurnal load cycle is assumed, then 

calculation of Emax per Equation 1 yields the theoretical largest energy storage unit necessary for 

maximum demand charge reduction. Although the load profiles of our examined facilities vary

day to day, we apply this equation to approximate Emax in favor of more complex approaches. 

Figure 1. Illustration of the maximum energy storage required for maximum theoretical demand 

charge reduction (Emax) in the presence of a perfectly sinusoidal diurnal load profile
4 

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Emmm = ∫ |L−Lmmmm|dd t2

t1

2( 2− 1)/24 Equation 1 

Where:

L = facility load at time t 

Lmean = average load over the time period t1 to t2

t1 , t2 = beginning and of time period examined in hours (must be greater than 24 hrs.; 

00:00:00 January 1 to 23:59:59 December 31 in this study) 

Figure 2 shows histograms of all available facility metrics, including Emax. The Emax metric 

shows some correlation with facility square footage, as seen in Figure 3. For commercial, 

educational, and food sales and storage facilities that fall below 40 watt-hours (Wh)/square foot 

(sq. ft.), we see a median value of 7.9 Wh/sq. ft. with an interquartile range spanning 5.3 to 9.7 

Wh/sq. ft. All metrics varied considerably over the data set by facility type and size, particularly 

for industrial and large commercial facilities, as shown in Figure 4. 

Figure 2. Histograms of facility properties

0 0.5 1 1.5 2

x 106

0

20

40

60

No. Facilities

Square Footage

0 2000 4000 6000 8000

0

10

20

30

40

No. Facilities

Peak Load (kW)

0 1 2 3 4

x 107

0

20

40

60

No. Facilities

Annual Energy (kWh)

0 5000 10000 15000

0

20

40

60

No. Facilities

EMax(kWh)
5 

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Figure 3. Distribution of Emax per square foot within sample facilities

Figure 4. Facility properties as a function of facility size and type

0 10 20 30 40 50

0

5

10

15

No. of Facilities

Emax per sq. ft.

> 40 Wh

/ sq. ft.

103104105106107

0

2000

4000

6000

8000

Peak Load (kW)

103104105106107

0

1

2

3

4

x 107

Total Energy (kWh)

103 104 105 106 107

0

5000

10000

15000

EMax (kWh)

Facility Square Footage

Commercial

Education

Food Sales & Storage

Light Industrial
6 

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2.3 Photovoltaic Generation

The addition of PV power to a facility under a net-metering rate structure will reduce energy 

costs. Further, it may also reduce demand charges if periods of solar power generation overlap 

with demand charge time periods. However, the intermittency of PV generation on partly cloudy 

days, as well as the occurrence of consistently cloudy days, is likely to reduce if not wholly 

eliminate any potential demand charge savings when operating under a rate structure with a 30-

day or longer ratchet (see Figure 5). The extent of this reduction will depend on the interplay of 

PV generation, facility load, and the action of energy storage when available. 

Figure 5. Illustration of the impact of solar intermittency and the noncoincidence of demand and 

PV generation on meter load. While the peak PV generation reaches ~160 kW, the peak metered 

load is reduced by only ~80 kW.

As such, it is important that we consider PV generation herein. To do so, we collected one year 

of PV data from the Los Angeles area for the same time period represented in the EnerNOC load 

data [9]. This provides some added realism for the Los Angeles facilities as it properly couples 

the incidence of solar radiation with ambient temperature and thereby facility heating, 

ventilation, and air conditioning loads. However we note that many of the facilities included are 

from outside the Los Angeles area, and accept that for these facilities coupling of PV generation 

with facility loads is not accurately represented. The degree to which this affects our results has 

not been quantified. 

The solar irradiance data were collected at 1-second intervals throughout the entire year of 2012. 

These values are translated to PV power generation by scaling the data using a simple fixed 

derating factor that encompasses panel efficiency and soiling such that the peak PV generation 

(alternating current output) is equal to 50% of the peak facility demand. No specific tilt, roof 

area, or change in orientation is assumed for any of the facilities, as the primary objective is 

simply to capture the temporal variability of PV power production. For this data set, a PV panel 

with a maximum output of 1 kilowatt (kW) generates 1,082 kilowatt-hours (kWh) over the full 

0 5 10 15 20

-200

-100

0

100

200

300

Power (kW)

Time (hour of day)

Demand

PV Generation

Aggregate Meter Load
7 

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12-month period. Select days of calculated PV power generation shown in Figure 6 illustrate the 

intermittency observed within this data.

Figure 6. Normalized PV power production

2.4 Battery Modeling & Control

BLAST’s simple zero-order equivalent circuit model was used to calculate battery voltage and 

heat generation as a function of current [5]. This model assumes a voltage source representative 

of the open circuit voltage of the electrochemistry in series with a resistor representative of the 

combined electrically and electrochemically resistive elements of the cell. Parameters are scaled 

appropriately with the energy level specified for the battery in each simulation. 

BLAST’s model predictive battery controller was employed for optimal peak-shaving, details for 

which can be found in [5]. The interval target memory was set to restart at the beginning of each 

day and a perfect 48-hour forecast of future loads was employed. The shorter memory 

encourages increased battery cycling, applied herein only to demonstrate the difference in scale

between savings achievable from shifting energy versus reducing power,1 while the perfect 48-

hour forecast enables optimal peak-shaving on successive days2

 and shows the maximum 

possible savings from such strategies.

3

2.5 Design of Experiments

A total of 98 facilities are simulated both with and without behind-the-meter PV power 

generation. When PV power is present, the Los Angeles solar irradiance profile is employed, and 

 

1 In practice, the value of this approach (increased time shifting of energy usage) would have to be weighed against 

the cost to the system (increased battery degradation). 

2 We found that use of end-of-day and shorter 24-hour forecasting windows would occasionally result in overly 

aggressive discharging in the near term, leaving the battery at an inadequate state of charge to address subsequent 

load peaks. Where diurnal trends exist, as is the case in the majority of our scenarios, increasing the forecasting 

window to 48 hours or longer is thusly preferred. 3 An accurate monthly forecast—if only an estimate of peak demand—could be employed to reduce battery cycling, 

efficiency losses, and thereby energy costs. However it would not affect demand charge savings, and the effect on 

energy costs is anticipated to be relatively small.

0 100 200 300

0

0.2

0.4

0.6

0.8

1

Day of Year

Normalized Power

164 165 166 167 168

0

0.2

0.4

0.6

0.8

1

Day of Year

Normalized Power
8 

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the nameplate PV power is scaled to 50% of the peak facility load. Each of these 196 scenarios is

then simulated with 35 different energy storage systems for a total of 6,860 unique cases. The 35 

different energy storage systems span seven different energy fractions and five different power 

ratings (Table 3). 

Table 3. Energy and Power Ratings of Battery Systems

Available Energy Fractions 0.5%, 1%, 2%, 3%, 5%, 7%, and 10% of Emax

Power Scale 

(minimum full available energy discharge duration)

30, 40, 60, 120, and 240 minutes
9 

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3 Results

Figure 7 shows the minimum reduction in the peak load across all facilities, PV installations, and 

battery energy scaling factors for two system power scales: the 240-minute system and the 30-

minute system. The performance of the longest duration system is bounded at an approximately 

85% meter reduction to installed battery power ratio, but achieves this metric frequently because 

it can sustain maximum power for up to 4 hours. The shortest duration system, on the other hand, 

is bounded by an approximately 60% meter reduction to installed battery power ratio for battery 

powers less than approximately 25 kW. At higher battery power levels, performance becomes 

more sensitive to the installed energy fraction and facility load profile, but generally the ratio of 

meter reduction to installed battery power falls.

Figure 7. Minimum reduction in peak load versus installed battery power for all simulated 

facilities, available battery energy, and both with and without PV. 

The dashed line shows the 1:1 ratio of equal meter load reduction and installed battery power.

Figure 8 shows the increase in annual energy consumption against the minimum reduction in 

peak load. This increase comes from the imperfect efficiency of the battery. The simulation is in 

essence a worst-case scenario with respect to energy usage, as the battery is encouraged to cycle 

each day. Thus, in practice a controller that seeks to minimize battery cycling for the same 

demand charge reduction would result in much smaller energy use increases.

The increased energy usage and allowance of on-peak charging can lead to increased energy 

costs, as shown by the cases with positive x-axis values in Figure 9. However, energy costs can 

also decrease due to the battery discharging mostly during on-peak times and charging mostly 

during off-peak times. The total number of cases simulated shows a slight bias towards reducing 

energy costs in this manner. However, in all cases, the economic savings of demand charges 

dwarf the impact on energy costs, often by an order of magnitude or more. 

0 500 1000 1500 2000 2500 3000

0

50

100

150

200

250

300

Battery Power (kW)

Minimum Reduction in Peak Load (kW)

240 min system

30 min system
10 

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Figure 8. Increase in annual energy use versus minimum reduction in maximum monthly meter 

load for all simulated cases (both with and without PV)

Figure 9. Impact of battery operation on energy costs and demand charge costs for all simulated 

cases (both with and without PV)

Figures 10 and 11 show the simple payback period of the energy storage systems installed in 

facilities without PV for the longest and shortest system durations, respectively. The cost of the 

complete installed storage system is assumed to be equal to $300 times the available energy in 

kilowatt-hours of the system plus $300 times the power in kilowatts of the system. This is 

intended to represent the cost of both a battery and inverter, where the cost of the battery scales 

largely with energy and the inverter with power, after the application of any available incentives. 

The payback period is then calculated as the total system cost divided by the annual utility bill 

0 50 100 150 200 250 300

0

1

2

3

4

5

x 104

Minimum Reduction in Peak Load (kW)

Increase in Annual Energy Use (kWh)

-4000 -2000 0 2000 4000

-14

-12

-10

-8

-6

-4

-2

0

x 104

Change in Energy Costs ($)

Change in Demand Charge Costs ($)
11 

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savings. Discount rates, rate structure evolution, depreciation, and other higher-order effects are 

explicitly ignored for ease of presentation. The results indicate that shorter system durations and 

lower energy scaling factors optimize payback.

Figure 10. Payback period statistics by stored energy fraction for a 240 minute battery system, no 

PV generation. 

Each box plot shows the minimum, 25th percentile, median, 75th percentile and maximum payback period across all 

98 facilities.

Figure 11. Payback period statistics by stored energy fraction for a 30 minute battery system, no 

PV generation. 

Each box plot shows the minimum, 25th percentile, median, 75th percentile and maximum payback period across all 

98 facilities.

0.005 0.01 0.02 0.03 0.05 0.07 0.1

0

5

10

15

20

240 min System

Energy Fraction

Payback Period (yrs)

0.005 0.01 0.02 0.03 0.05 0.07 0.1

0

5

10

15

20

30 min System

Energy Fraction

Payback Period (yrs)
12 

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Figures 12 and 13 show payback periods calculated in the same manner for facilities with 50% 

PV. The results are strikingly similar. In Figure 14, we explicitly compare the cases with and 

without PV using the median payback period across all 98 facilities. This shows nearly identical 

trends with respect to absolute payback period and optimal energy storage system sizing. 

Figure 12. Payback period statistics by stored energy fraction for a 240-minute battery system, 

50% PV generation. 

Each box plot shows the minimum, 25th percentile, median, 75th percentile and maximum payback period across all 

98 facilities.

Figure 13. Payback period statistics by stored energy fraction for a 30-minute battery system, 50% 

PV generation. 

Each box plot shows the minimum, 25th percentile, median, 75th percentile and maximum payback period across all 

98 facilities.

0.005 0.01 0.02 0.03 0.05 0.07 0.1

0

5

10

15

20

240 min System

Energy Fraction

Payback Period (yrs)

0.005 0.01 0.02 0.03 0.05 0.07 0.1

0

5

10

15

20

30 min System

Energy Fraction

Payback Period (yrs)
13 

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Figure 14. Median payback period across all 98 facilities as a function of battery system energy 

and duration. 

Solid lines indicate cases without PV; dashed lines indicate cases with 50% PV.

Figure 15 presents the payback period minimizing system energy fraction and duration for each 

of the 98 facilities analyzed, showing that the shorter duration and lower energy fractions 

assessed were heavily favored. Figures 16 and 17 present the distribution of these best-paybackperiod systems in terms of kilowatts and available kilowatt-hours. We find that 75% of the bestpayback-period systems are less than or equal to 30.5 kW and 15.5 kWh, with median values of 

14.2 kW and 7.86 kWh, respectively. Compared to the median peak facility load of 569 kW, it 

appears that demand charge management with storage is most cost effective when the size of the 

storage system is small. 

The presence of PV generation has not significantly affected the optimal sizing of a DCM energy 

storage system (Figure 15) nor the median payback period when the system is so optimized 

(Figure 14) across the range of variables explored. However, while Figure 16 shows that a large 

concentration of facilities see little to no impact of PV on the value of a battery providing DCM 

services, it also shows that PV can decrease this value. In fact, it shows that the distribution is 

slightly biased towards the negative side (57 of 98 facilities), implying that the value of batteries 

for DCM is more likely to be degraded by the presence of PV generation than to be increased. 

Figure 18 shows the effect of the presence of PV generation on the value of a 0.5% energy scale, 

40-minute power scale battery in a DCM role. 

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

2

4

6

8

10

12

Battery Energy Scale Factor

Median Payback Period (yrs)

240 min system

120 min system

60 min system

40 min system

30 min system
14 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 15. Best system energy and power levels for facilities without PV. 

Sixty-one of the 98 facilities elected the lowest energy fraction (0.5%) examined in this study. Results for cases with 

50% PV (not shown) are nearly identical.

Figure 16. Distribution of payback-period-optimized battery system power levels (no PV)

0.005

0.01

0.02

0.03

0.05

0.07

0.1

240 min

120 min

60 min

40 min

30 min

0

10

20

30

40

Energy Fraction System Duration

No. of Facilities

0 50 100 150

0

5

10

15

Payback-period-minimal Battery Power (kW)
15 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 17. Distribution of payback-period-optimized battery system energy levels (no PV)

Figure 18. Effect of the presence of PV generation on the value of a 0.5% energy scale, 40-minute 

power scale battery in a DCM role

0 20 40 60 80 100

0

5

10

15

20

25

Payback-period-minimal Battery Energy (kWh)

No. of Facilities

-60 -40 -20 0 20 40

0

5

10

15

20

25

Change in Battery-Generated Savings When PV is Added (%) No. of Facilities
16 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

4 Discussion

4.1 Battery Sizing

Our simulations have shown that batteries can effectively reduce peak metered power of realworld facilities. Further, for installed systems costs of $300/Wh and $300/kW, the use of storage 

batteries results in many encouraging payback periods less than 5 years long, and payback 

periods less than 3 years long for optimized systems. Thus, where technology and incentives 

combine to reach this price point, the deployment of energy storage for demand charge 

management could become rather commonplace. 

However, even with 4 hours of energy at a system’s maximum power output (the maximum 

duration considered here), it should not be expected that the system will reduce the peak interval 

load at the meter by the full power value of the battery system every month of the year. We find 

that 85% of the rated system power is a more reasonable expectation when the rated power 

duration is 4 hours. As system duration is decreased (power to energy ratio is increased), this

percentage falls substantially and becomes more sensitive to the nature of the facility’s power 

demands. 

Despite the superior ability of longer duration systems to yield peak power reductions closer to 

the rated maximum system output power, shorter duration systems are superior where 

minimizing the payback period is concerned. This is a result of the nature of typical load profiles, 

where the amount of energy necessary to provide a given reduction in peak load increases 

nonlinearly with the magnitude of the peak load reduction. For example, reducing peak load by 

10 kW from its original value may require only 5 kWh of energy, but reducing peak load by 20 

kW from its original value may require 15 kWh of energy: doubling the peak power reduction 

requires tripling the available energy. Because system cost is proportional to power and energy 

levels, the total cost of the system increases nonlinearly while its value (reduction in utility bill) 

only increases linearly, and the payback period increases. Minimizing payback thereby drives 

systems to higher power-to-energy ratios that operate at lower values of absolute facility peak 

load reduction. While the higher relative variability in facility load profiles at these power levels 

decreases the ability of the system to consistently reduce peak meter loads by the maximum 

system power, the economic impact of this phenomenon is secondary in nature to that described 

above. 

Optimizing battery system parameters for payback period therefore results in selection of both 

the smallest duration and energy values considered here (durations of 30 and 40 minutes, energy 

fractions of 2% and smaller). The resultant power and energy ratings of the optimum system 

exhibit sound correlation with the Emax metric; however, the limits of this specific parametric 

study could have influenced the trend, i.e., optimum system configurations were found at the 

minimum power and duration metrics examined in this study. 

However, while these small systems are shown to result in optimal payback periods, the absolute 

economic savings may not be a sufficient incentive for facility owners and operators to install 

such hardware. In practice, it may prove more common that larger energy storage units are 

installed, trading longer payback periods for larger absolute savings. Fortunately, Figures 10–15 

suggest that many facilities can significantly increase battery energy above the optimal values 

identified herein with only small increases in payback period. Installation of larger systems 
17 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

therefore could be of benefit to both the customer and utility. This could also be strongly affected 

by available incentives, such as [4] which encourage systems with a 2-hour minimum duration at 

rated power. 

4.2 Interaction with PV

Interestingly, optimal battery system specification had little to no sensitivity to the level of the 

installed PV generation. This was found to be true across the entire range of battery power and 

available energy studied. This is most likely because PV generation has minimal impact on the 

nature of the aggregate load profile when viewed on the order of an hour or less (where our 

optimal systems generally operate), and thus has only marginal impact on optimizing system 

size. 

However, the presence of PV generation can affect the value added by a battery. While the 

addition of energy storage is found to increase total utility bill savings regardless of the presence 

of PV generation, it appears that adding storage to a facility without PV is likely to be a more 

valuable venture than adding storage to a facility with PV. The root cause for this is most 

probably the fact that PV alone can capture some demand charge savings, leaving fewer savings 

to be harvested by storage. The importance of this effect is debatable, though, as many facilities

see little noticeable impact of PV on the value of storage. Further, trends could change 

significantly when large amounts of PV are installed, backfeeding power through the meter is 

restricted, and/or financial incentives are offered for pairing storage with PV. 

4.3 Effect on Energy Consumption

Total consumption is increased due to the inefficiency of the battery system in all cases. 

However, there is a strong tendency to reduce energy costs because the battery typically 

discharges to reduce peak loads during on-peak times where energy prices are high and charges 

during off-peak times when energy prices are low. The magnitude of energy cost effects is much 

smaller than the effects on demand charges.

4.4 Implications for Utilities

The systems that minimize payback period also result in small reductions in facility peak power, 

on the order of 2.5%. The continuous duration of these reductions is generally one interval: 15 

minutes. Encouraging the deployment of such systems may be of limited benefit to the utility. 

Demand charge rate structures are generally designed to reduce capacity requirements for onpeak generators and reduce or defer investment in transmission and distribution assets However,

when employing batteries to effectively smooth load fluctuation of individual facilities with 

periods on the order of minutes (as our value-optimized systems are prone to do), it is possible 

that utilities will see little to no effect in the cumulative peak load of multiple facilities upstream,

providing little opportunity for the utility to reap costs savings via reduced investment in 

transmission, distribution, and generation assets. This effect is demonstrated in Figure 19, 

showing how the approximately 2.5% reductions in peak load at the individual facility level 

translates to only approximate 1.5% reduction in peak load when all 98 facilities are aggregated. 
18 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

Figure 19. Effect of behind-the-meter batteries on aggregated facility loads

Encouraging installation of larger systems that result in larger decreases in the peak loads of 

individual facilities could address this matter. It may also be in the interest of facility owners and 

operators—while the smaller, short-duration systems minimize the payback period, the smaller 

utility bill savings may not be viewed to be worth the effort. Incentive programs that enforce a 

maximum power-to-energy ratio of 1:1, resulting in minimum durations of 1 hour, would be one 

option for achieving such an end. 

4.5 Opportunities for Vehicle-to-Grid Service

On the other hand, identification of high power-to-energy ratios as economically optimal is 

encouraging for the use of plug-in electric vehicles (PEVs) and bi-directional charging 

infrastructure for demand charge reduction. This is particularly relevant for workplaces 

considering the addition of chargers for their employees: access to chargers could provide 

considerable benefit for select plug-in electric vehicle (PEV) drivers [10], while access to battery 

capacity in the PEVs could offer a more cost-effective approach to demand charge reduction than 

the installation of dedicated batteries to facility operators. One major concern in this space is the 

amount of energy that would be depleted from connected vehicles, thus reducing the electric 

range of the cars for the drivers. This is of most concern for battery electric vehicles that do not 

have the ability to continue driving once their electric range is exhausted. Our analysis shows,

however, that as little as 30 minutes of storage can provide high value to the facility. Thirty 

minutes on a 93% efficient Level 2 charger (max 6.6 kW alternating current) corresponds to a 

relatively small 3.5-kWh accessible energy window required from the automobile. If the driver 

disconnects the vehicle immediately following a 30-minute peak shaving event, approximately 

10 miles of range would be lost. Accounting for charger efficiency, replenishing this same 

amount of range requires approximately 35 minutes. Thus, if a vehicle arrives with more than 10 

miles depleted from its maximum range and is parked at a charger for longer than 65 minutes 

total, it is likely that the vehicle will leave the charger with more energy than it arrived with. This 

provides value to both the driver and the facility operator. These numbers encourage additional 

analysis to fully quantify these values, which could in turn encourage policy supportive of 

deploying bi-directional workplace chargers to both increase penetration of PEVs and decrease 

emissions for grid electricity.

0 50 100 150 200 250 300 350

0

0.005

0.01

0.015

0.02

Day of Year

Reduction in 15-minute Average Load (frac)
19 

This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

In the same light, commercial PEV fleet operators may find considerable benefit from DCM 

through the use of large commercial PEV storage capacity (on the order of 40 to 80 kWh), either 

in the context of bi-directional power flow or, more simply, managed charging. Especially for 

those fleets operated by the same entity as the depot facility manager, utility bill savings can 

provide common motivation for both parts of their business. Even more, those fleets operating 

regular routes—as commonly found in several delivery industries—can provide a highly 

predictable energy storage service, provided the facility peak demands align with periods that the 

fleet is parked at the depot and either available for discharge (in the case of bi-directional power 

flow) or charging (in the case of managed charging). The National Renewable Energy 

Laboratory is presently investigating these opportunities with early commercial fleet PEV 

adopters. 

4.6 Opportunities for Battery Second Use

Additionally, the observed distribution of energy and power levels is also advantageous for the 

second use of PEV batteries, where automotive propulsion batteries are removed from the 

vehicle at the vehicle’s end of life, repurposed, and deployed in a different application until the 

end of the battery’s useful life [11]. The small resultant storage systems size—largely less than 

15.5 kWh and 30.5 kW—could readily be met by a single repurposed battery electric vehicle

battery (typically providing greater than 20 kWh and 90 kW at beginning of life), or even 

modules there from. This would largely negate the challenges of integrating large energy storage 

systems from multiple used battery electric vehicle and plug-in hybrid electric vehicle batteries 

of various states of health, cell or module design, chemistry, etc., and thusly support the low cost 

of second use batteries projected by [12]. Policies encouraging second use of automotive packs 

in this way would have similar effects as encouraging vehicle-to-grid, but also delay battery 

recycling requirements, which provides additional environmental benefits. 

4.7 Projecting Potential Market Size

Even under a small battery scenario, the total market size for such batteries could be quite large 

as well. Our observed relation of Emax to facility size (7.9 Wh/sq. ft.) combined with a 40-minute 

system with a 2% energy fraction yields a coarse rule of thumb of 0.24 watt/sq. ft. and 0.16 

Wh/sq. ft. for sizing DCM batteries. Applying this relation to facilities in the United States [13] 

exclusive of those smaller than 25,000 sq. ft., suggests a market potential of 7,100 megawatthours and 10,700 megawatts (MW) of behind-the-meter DCM batteries, assuming all facilities 

become subject to a demand charge tariff. While our market projection is smaller than a previous 

Sandia National Laboratories estimate (32,000 MW) due to our assumption of small, paybackperiod-optimizing batteries, it is still nearly an order of magnitude larger than the much 

discussed frequency regulation market (1,000 MW) [14]. Such findings may be valuable for 

current California utilities seeking to find uses for the 1.3 GW of storage they are in the process 

of procuring [1]—properly deployed and utilized, increased behind-the-meter storage may be an 

efficient and effective pathway to reaching their target. 
20 

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5 Conclusions

Our analysis has shown that small battery systems capable of fully discharging in 30 to 40 

minutes offer optimal payback periods of less than 3 years when installed costs reach $300/kW 

and $300/kWh, respectively. While these price levels may well be lower than what is necessary 

to purchase suitable hardware today, research and development efforts to reduce battery [15] and 

inverter costs [16] are aggressively pursuing price points considerably less than our assumed 

values. Further, the application of utility-provided incentives can strongly reduce net costs to the 

user [3, 4]. It also appears that use of PEVs for DCM via vehicle-to-grid service may be 

attractive to both PEV drivers and facility operators as an alternative to dedicated stationary 

storage. Thus, employing energy storage in one form or another for behind-the-meter DCM may 

be economically viable for select customers today, and to vastly increasing amounts in the future, 

ushering in a time where customer-sited DCM energy storage is common place.

It is important to note, however, that this study assumes perfect forecasts for demand and PV 

generation. Economic sensitivity to forecast errors, while not explored herein, could be high, as 

such errors could result in commanding too little action from the battery system when meter 

loads are over predicted, or in running out of battery energy during peak load times when meter 

loads are under predicted. While conservative use of battery capacity could address forecast 

error, it will effectively reduce the savings generated by a given system. On the other hand, many 

commercial facility loads are strongly subject to local weather conditions, which are reasonably 

predictable, and forecast periods need only be on the order of 48 hours. Thus, the forecasting 

challenge may likely be surmountable. 

While these factors would all suggest future widespread use of energy storage for DCM, it is 

important to recognize that utility rate structures are subject to change. As more customers begin 

reducing peak loads with energy storage and other methods, it is possible that the price per 

kilowatt placed on peak loads by utilities will fall, making use of costly hardware less 

economically attractive for this task. Competing trends in wind and solar power, distributed 

generation, and smart-grid technologies make prediction of demand charge trends challenging 

over the long term. However, where the cost of energy storage is relatively high and payback 

periods are longer, sensitivity to such fluctuation will increase. Accordingly, policy to stabilize 

rate structures for customers over the periods of time similar to anticipated payback periods 

would encourage the use of this technology. 
21 

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